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Leveraging natural language processing models including transformers, we curate four distinct datasets: tmCAT for catalysis, tmPHOTO for photophysical activity, tmBIO for biological relevance, and tmSCO for magnetism. 

We report that differences in ring strain enthalpy betweencisandtransisomers of sila-cycloheptene provide a driving force for both polymerization and depolymerizationviaolefin metathesis. 

The aza Paternò–Büchi reaction is a [2+2]-cycloaddition reaction between imines and alkenes that produces azetidines, four-membered nitrogen-containing heterocycles. Currently, successful examples rely primarily on either intramolecular variants or cyclic imine equivalents. To unlock the full synthetic potential of aza Paternò–Büchi reactions, it is essential to extend the reaction to acyclic imine equivalents. Here, we report that matching of the frontier molecular orbital energies of alkenes with those of acyclic oximes enables visible light–mediated aza Paternò–Büchi reactions through triplet energy transfer catalysis. The utility of this reaction is further showcased in the synthesis ofepi-penaresidin B. Density functional theory computations reveal that a competition between the desired [2+2]-cycloaddition and alkene dimerization determines the success of the reaction. Frontier orbital energy matching between the reactive components lowers transition-state energy (ΔG^ǂ) values and ultimately promotes reactivity. 

Mechanical force drives distinct chemical reactions; yet, its vectoral nature results in complicated coupling with reaction trajectories. Here, we utilize a physical organic model inspired by the classical Morse potential and its differential forms to identify effective force constant (keff) and reaction energy (ΔE) as key molecular features that govern mechanochemical kinetics. Through a comprehensive experimental and computational investigation with four norborn-2-en-7-one (NEO) mechanophores, we establish the relationship between these features and the force-dependent energetic changes along the reaction pathways. We show that the complex kinetic behavior of the tensioned bonds is generally and quantitatively predicted by a simple multivariate linear regression based on the two easily computed features with a straightforward workflow. These results demonstrate a general mechanistic framework for mechanochemical reactions under tensile force and provide a highly accessible tool for the large-scale computational screening in the design of mechanophores.